All right, well, I wanted to, one, thank you all for giving us an hour and a half of your time today. I know that sounds like a long time, especially in today's world, but I can promise you that our presenters have some amazing content, and we really wanted to set it up this way so that we had an opportunity to, one, keep the session interactive, two, really cover a lot of the bases that we felt like were important as we had this conversation on generative AI, and three, actually have some opportunities to really do some demonstrations where we're asking you to follow along. So hopefully that sounds like a great plan for you. I see the chat coming through. So again, so happy to see so many of us join, so many of you joining us today. I am Jenny Cooke-Smith. We'll do some formal introductions in just a moment, but just a couple of housekeeping items as we're getting started. I think you've already found the chat. So let's keep this session interactive throughout. I think that makes it the most fun. We will also have opportunities to have some time to answer some of your questions. And in fact, what we'll do is sort of have some check-ins throughout, so we have an opportunity to see what's on your mind. And again, we have some amazing presenters. They're fantastic on this topic, and so we really do hope you'll come to us with questions. But without further ado, I am actually going to pass things over to our CEO and President Sue Cunningham to share with you a few thoughts as we get ready for today's webinar. Advancing education transforms lives and society. For 50 years, CASE has been the convener of the advancement profession, defining the competencies and standards for the profession, and leading and championing their dissemination and application across the world's educational institutions. Over the past year, as we have celebrated our 50th anniversary, we have been asking what the future of the profession will look like across all the advancement disciplines, alumni relations, communications and marketing, fundraising, and advancement services. One key message we heard repeatedly was the importance of the role of artificial intelligence and how that will play out across all of the disciplines and how it is already being used. With the collaboration and support of the CASE 50, CASE is embarking on a research project to understand how members can best integrate artificial intelligence for positive impact, beginning with this training. Following this training, your institution will be invited to participate in a study that will identify adoption strategies and best practices for using artificial intelligence in educational advancement. Your participation in this study is essential and will help us stay on the leading edge as a community. We are thrilled that you are interested in this work. Our goal for the effort is to understand how AI is currently being used and determine how it can best support advancement in the future. For more than 50 years, CASE has convened the profession around key topics and has trained practitioners in the art and science of advancement. Thank you for helping us with this important initiative for the advancement profession. There we go. So let's take a break and talk a little bit about our session today. So our Vice President of Data Research and Technology, Cara Giacometti, will actually kick us off talking a little bit about CASE Insights and how this work fits in to the greater mission within CASE. And then what we're going to do is a bit of a panel discussion really focusing on adoption, what we've seen so far, and a little bit around this idea of generative AI and the hype cycle and some thoughts of where we are in those stages. We'll then transition to demystifying generative AI. Kristy Moss is going to talk through some of the terminology that we all hear out there and really make sure we have a good understanding of some of those fundamentals. Focusing on, as I mentioned earlier, some demonstrations for you to do on your own. We'll then talk through use cases and sort of use cases that you can think of no matter what type of institution you're representing and no matter where you are in the world. And then finally, and I'm incredibly excited about this, we're going to give you an opportunity to take part in the research that CASE is doing. And I want to take a moment and say this research and this training is all possible thanks to the generous support from the CASE50. The CASE50 are essentially CASE's largest fundraising institutions in the world. And they have a research fund and felt that this was such an important topic and a topic that was really pervasive across our industry, that they've denoted this research opportunity for us. And so again, we'll talk a little bit more about what that entails and give you an opportunity to take part. So I think I accidentally, there we go. So let's actually take a moment and introduce our speakers today. So you're going to hear from Kara in just a moment. And again, Kara is our VP of data research and technology at CASE. You've already been hearing from me, but I am on Kara's team and I'm our senior director of CASE Insights Solutions. But I'm really excited to introduce you to our two experts in this topic today, Christy Moss and Gustavo or Gus Seguin. Christy is the vice president of marketing at the University of Illinois Alumni Association. And she's also an adjunct instructor at their top ranked iSchool at the University of Illinois. Christy has a career spanning over two decades. I think I've been fortunate to actually know Christy for most of those two decades. She's held pivotal roles across university administration, including marketing advancement and alumni relations, and also serves as a thought leader in these areas. In this past year, Christy has authored numerous insightful pieces on generative AI and has conducted over a dozen keynotes and workshops. She also writes the popular newsletter AI Insights, which provides higher education professionals, faculty and students with transformative ideas on integrating generative AI into their work and studies. And again, you're going to hear some of those today. And I'd also like to introduce you to Gustavo again or Gus. Gus serves as the executive director for the Marketing Advancement and Admissions at the International School of Curitiba in Brazil. In this role, he establishes a strategic framework for business growth and innovation, leveraging tools from AI, strategy and design thinking. As a strategy advisor, Gus is a board member of the American Chamber of Commerce in Brazil and a council member for a CASIS Latin American regional council. As an international speaker, he's given workshops in several conferences in the US, UK, Mexico, Colombia and Brazil. And he has an MBA in business management and certificates from Harvard, Stanford and Alt MBA. And his theoretical expertise and professional experience and leadership strategy and innovation really help him navigate complex environments and translate insights and the strategic planning and results. So you see this great theme between both of our speakers and taking really complex topics and making it easy and actionable. And that's exactly why we're so excited to have Gus and Christy join us today. But first, I am going to pass things over to Kara to talk a little bit more about Case Insights. Thank you so much, Jenny, and thank you all for joining us today. It's wonderful to see so much energy around this topic. I know it's one that's been exciting me for some time. But before we jump in, I want to share a little bit about Case Insights. Case Insights is a primary resource and service provider for data, benchmarking, analytics and original research for the educational advancement community. It was launched by Case in 2018. And since then, we have expanded our offerings to all aspects of advancement. We maintain the case global reporting standards for the profession, which is now in its second global edition. And we have offerings focused on philanthropy, alumni engagement, campaigns, diversity, equity, inclusion and belonging, and marketing and communications. And you can find out more about all the Case Insights offers at our website, which is provided here, just looking at the case website. Next slide, please. Our work falls into three main categories. We have data, our benchmarking surveys, which we're often most known for, which have expanded over time to encompass more topics and more regions and will continue to expand. This year, we're piloting a philanthropy survey for Latin America, for instance. So we've been slowly kind of filling in the variety of data that we're collecting. We're responsible for the standards that we keep up to date and are continually refining. And then the third area of our work is research, which are deeper dives into topics of interest, which are often offered through investments from funders and sponsors to help us bring new insights. And today's session falls into that third category and we'll be doing additional research on this topic. And it was funded by our Case 50 Research Fund. So thank you all for joining and excited to jump into the topic. Great, thank you, Kara, and as you all see, I'm just going to pause for a moment. One, I've been talking a lot and I really want to hear from each of you. And so as I think you all heard from the amazing bios of our guest speakers, as well as the fact that Kara and her role is really leading CASE's own adoption of AI and thinking about the needs for our CASE members on this topic. I'm actually just going to start with a very basic question, which is, why are you here? Why is this a topic that you've really sort of jumped in the space of early adopters? And maybe, Gus, do you want to kick us off? Sure, thanks, Jenny. Hello to everyone. Thank you so much for joining us today. We're very excited to share some information, some experience. And I appreciate that you're taking the time to be here and listen to what we have to share. So why AI? I think that's such a good question and everyone might have their own kind of idea. But I think for me personally, I'm just an enthusiast of technology. I've always been an early adopter as an individual. I'm very excited with new things and trying and I'm a very curious person as well. But I think speaking from a small shop as an international school and not just like massive institutions and we see out there, we're a very small team with very big ambitions, I would guess. And as a leader, I'm always looking at opportunities for us to be more productive, to be more effective, to balance our high goals with a work balance as well, which is very important. And I think two years ago, we were ready to to become a data driven kind of department as a school in the learning side, but also in my areas as well. And I think when we're thinking about how can we be more effective? How can we be more productive? How can we use tools? I think AI just presented to be this great avenue to find alternatives of work, of improving work, of even like sometimes filling in some skills gaps, you know, like in the team. And there's so much as a small team with very limited resources, there's so much we need to embrace and to accomplish, which I think then AI presented this great opportunity for us to to experiment, to try things out and and to do some the work a little bit different than we used to do in the past. And this is just like an exciting journey of discovery and experimentation and and then finding the results we want with that. So that's a little bit of why I'm super keen and interested in this. Well, and I love that because it actually hits on a lot of the reasons why CASE is conducting this research as well. And again, another reason why we were really thrilled to partner with you on this project. Christy, same question. Well, I, unlike Gus, I'm not usually an early adopter, I'm usually a little bit nervous around these things, and I kind of put on my professor hat and I have some ethical concerns sometimes, too. But I have to say, I had a friend who jumped in first and said, listen, for productivity, you really have to check this out. So I started with this idea of productivity and then I realized pretty quickly how much it was going to revolutionize not just our business, but also education itself. And I joined a group at the University of Illinois called the AI Solutions Hub with researchers and faculty and administrators from across campus to look at different use cases and really pilot this in different areas. And what it became really clear in this case was that folks were hesitant because they didn't know how it worked. Like this is a black box thing. And that kind of makes people scared and nervous. And kind of part of my background, as Johnny was mentioning, is in professional development for folks who are in advancement and marketing. And so I said, you know what, if I'm scared about it, it probably means others are, too. So let's dive in and figure out how this works and then hopefully share that information with all of you. It's hard to imagine the Kristi Moss we know today being scared of AI, so I think that's a great story of learning and really how far we can come. Kara? Well, it's interesting because my entire career, I've been working on projects that were always expanding something new, adding new data we were collecting or new ways of thinking about products or services at an institution or at CASE. And if you're going to keep expanding in those ways, I've never had any position in my life where the resources kept up with the ambition. And once you've maintained something and done something, you have to keep it going. It's really hard to sunset things people have become really comfortable with, especially when you're working in a research and data space like I have been. And so the way to have the space to do new things is to figure out ways to streamline and automate what you've been doing already and to build from there. And I see AI as a real asset in that space, that that's really the strength in taking things that you've done a lot of the brainwork on and you can help make it faster, more seamless as you move forward or to take some of the rote tasks out so there's space for that higher level of thinking. So I think we're just beginning to unlock that possibility, but it really seems a game changer for any group that's really trying to continue to innovate and do more, whether you're in a tiny program or you're just the team that's on the cutting edge of the larger institution that you're at, where you don't have those all of those systems and resources yet behind you. Kara, I'm going to actually stick with you for a moment. You know, this is this project to CASE in many ways is sort of the second part of ongoing research. And so can you talk a little bit about, you know, where where we are in terms of adoption and what CASE has learned as part of this process? Yes, I'm happy to. So earlier this year, we did a different sponsored research project that went through generous sponsorship from Give Campus, where we did a poll survey that many of you responded to where we asked advancement professionals anywhere around the globe how they were using AI. And it was really fascinating because what we found is that most institutions, we found that individuals were using AI, but we weren't seeing AI adoption broadly across the kind of strategic mission of the institution. It was people trying things out on their own, usually for personalized outreach. That was the first use case we saw. We saw some other writing tasks and a little bit of exploration into data and insights. But the thing that really struck me is that in almost every case, this was an individual driven exploration rather than a university initiative or a school really having a strategic plan that individuals were participating in. And so that's really caught my attention. And it's something we've been trying to give institutions more tools to grapple with. And there's a little more about this research that I thought was interesting. If you want to go to the next slide, which is when we asked people if they had a policy in place about how to use AI or ethical guidelines provided by their institution, 72 percent were unsure or unaware of such guidelines. Now, we've seen some increase in institutions adopting guidelines since we conducted this research earlier this year. This is a very fast moving space, but I think there's still a lot of opportunity here to think about how are we setting up the conditions of success for these programs. And so when we did this research and we found that most use in institutions where individuals exploring on their own, mainly for writing type of tasks or personalized outreach without a lot of structure or guidance from their institutions, then this is a space where the case thought we could provide more support. And I'll just also add, I thought one of the more interesting findings from this were that small minority that actually did know that guidelines existed did also indicate there was an increase, a higher frequency of thinking about those ethical considerations whenever their AI was in use, to go back to Christie's point at the beginning. So I think it makes sense. But we certainly see that just the fact of having that policy and that training meant that people were starting to employ some of those pieces as well. And Gus, let's let's actually go to you a little bit, because Kara's talking a little bit about this idea of adoption and where we are and some of the things are happening, but not necessarily always at the institutional level. Can you shed a little bit of light on where we are in terms of hype cycles and then where we are right now as we think about generative AI in terms of adoption? Sure. Thanks, Jenny. So as we were getting together and thinking for this presentation for you, we thought that, hey, there is so much information out there about AI and it seems that everyone is using and AI is used for everything. And it's like the silver bullet that will solve all the problems. And then we decided, like, I think it's good to start this conversation with a very objective view of where we are at and really like cut through the bus and share with you where we are now in terms of the technology adoption kind of overall in the world. So if you go to the next slide, Jenny, this is the hype cycle, which is a graphic model. Maybe some of you are familiar with that already, but I just want to to go through it. And it shows how technology, maturity, adoption and social use change over time. And this was done by the American research firm called Gartner. And they developed this to help people understand kind of the life cycle of any technology. And today we're going to share with with you what each stage means and a little bit where generative AI is. So if you start with innovation trigger, this is where technology kind of there is a breakthrough or a product launch that gets people really talking about. So we saw that with OpenAI when they just launched ChatTPT and how much kind of news and information and conversations was about. Then as this starts to get traction, there is a peak of inflated expectations, which there is more hype actually than proof that innovation can deliver what you need. So as people start to using, they get kind of the reality check and we were and often technologies go through the through of disillusionment, which is this original excitement wears off and early adopters report performance issues and sometimes low return on investment. And they were trying to figure out actually how this technology can can help and address the needs and the issues that we have. So after some time into that, we go through the slope of enlightenment, which others starts to understand how to adapt the innovation to their organizations. So this we see it goes a little bit from just like the individual use level, but like broader institutions start to say, hey, there is this is important for our strategy. This is important for our strategic advantages. Let's see how we can implement is and start using on a strategic organizational level. And after that, with some time to that, we get to the plateau of productivity, which is a point in time at which more users see real world benefits and innovation really goes mainstream as a as a tool. I don't know, like Photoshop if you are in design, right? It's super used and really well established in the scenario. So if you think about generative AI, where would you put Gen AI in this graph? Right. We are seeing now that generative AI is transitioning from this peak of inflated expectations. So that's why we see so much on Instagram or TikTok about this. And earlier experiments really kind of yielded mixed results. But we are seeing that. And from this report from Gartner, we expect that Gen AI will take about two to five years, if you just give one more click, Jenny, I think it will show, so it will take about like two to five years for Gen.AI to achieve the the plateau of productivity where I think widespread adoption with more consistency and kind of depth of use in organizations and also like a better understanding of return on investment and I think this is which makes a great perfect timing for us to be having this webinars to having case research and really kind of talked about that as we go through this together right so this is kind of where we are right now and then I'm going to share now with Christy where she'll talk a little bit about the impact of generative AI and a little bit of kind of this journey of implementation institutions as we see yes and Virginia in the chat asked a question about adoption and you're right Virginia adoption really is uneven and when we come out of that into that productivity area it's when adoption starts to even out a little bit more and kind of the best practices around how we get there is educating ourselves and learning kind of how these tools work but this is kind of looking at this from an institutional perspective and thinking about it as a little bit of a maturity model so a lot of organizations are in this getting started phase which is where there are some folks in the organization that are those early adopters the champions they're already using generative AI kind of on their own even if there aren't really any company policies about it we're starting to see more and more places using custom solutions this is when you take a model like chat GPT and you start to create custom GPTs and we'll have an example of that later in the presentation but it's something that's specific to your body of knowledge or whatever your knowledge platform is so that plus generative AI gets you some custom solutions where we are moving in the future and frankly I haven't seen any higher education advancement shops get there yet is this idea of transformative where we set up we use generative AI to set up a series of tasks that then AI agents act on our behalf and the technology frankly it's not ready for us to be there quite yet so right now most of us who are pushing things forward are in this area of custom solutions so we thought it would be helpful to hear from all of you we've shared some ways that whether it's case data or larger data we're seeing institutions and really more specifically individuals use AI and so you should see a poll popping up asking you how you are using AI all right and we have it looks like we have about 117 people that completed this thank you very much and as you can see the majority are in that donor communications personalized emails though I'm very close behind with those of you not actually using AI followed by solicitation of funds I do see one person indicated financial management and fraud detection love to hear a little bit more about that in the chat this is actually the first time we've or the second time we've run this poll and we did not have anyone respond to that and I see a great suggestion for continuing to expand to this do see some chat bots we're certainly hearing that and then I'm great to see that impact measurement and analysis Gus do you want to talk a little bit about what you've seen in your your research yes absolutely so I think this is just great because we see how how attendees are using AI but we thought that you know what does the the other side look like and where there were there is something really interesting information to share and you have access to this report with within the package that we have delivered for you so I just want to share like this you can go to the next slide Jenny please so this donor perceptions of AI reported where they surveyed a thousand donors towards artificial intelligence and they just come up with this question of how comfortable are you with charities using AI in the following areas so as you see these results think about your own organization and this is likely questions from one being very feeling very uncomfortable and five feeling very comfortable and you can see that solicitation of funds like fundraising appeals this is most donors are most uncomfortable with followed by donor communications which is a little bit and there is some level of comfort feeling comfortable regarding financial management and fraud detection program delivery and impact management and analysis what we see this is really like the lower comfort levels suggest a need for more gradual and transparent approach to AI integration in these areas I think like leveraging AI for efficiency and impact organization should maintain traditional engagement methods to accommodate donors less comfortable with AI driven interactions and you and in the report you can see more information which things is a very interesting information because we see more and more institutions using AI for communication and solicitation of funds and this is where on the other side donors feel a little bit less comfortable with and given the higher comfort levels with AI in impact measurement financial management institutions might prioritize AI implementation in these areas to build donor trust and demonstrate value first so implementing the donor educational programs we saw that might be might help increase overall comfort levels and acceptance of AI driven initiatives so this graph will show like the donor side of things and it's a little bit the inverse of what we see how institutions have been adopting and just it's just like great information for some strategic conversations around AI adoptions and then the next slide we looked for in this report for transparency in AI usage and this is we cannot like underline the importance of being transparent about 69 percent of respondents said that this is very important while 23 percent said that it is somewhat important five percent not very and two percent couldn't really care less about institutions using AI but this is like this this figure is really underscored a significant mandate for transparency it's a nearly 93 percent of respondents considered important and I think for us as an institution using AI we really have like this trust equation where finance suggests that donor trust in AI is not a simple function of familiarity with the technology I think but rather a interplay of transparency demonstrable impact and a kind of a preserved human connection donors don't want to just be interacting with chatbots so the future of AI implementations must really address this multi-variable equation in order to succeed and as we are just kind of getting into it I think there is yet to learn a lot what would trigger donor reactions as we implement this and we see how things go on from from now on right so I think that's we want to share this information as addressing some really interesting conversations in your institutions and perhaps in the group chat as well so thanks thanks Gus I'm just going to pause our presentation a moment before we move to the next topic I've been trying to keep up with the chat which was actually quite difficult because you all are giving us some fantastic information in there let's take a moment though and one I should have prefaced at the beginning you all are using the chat perfectly to share and comment but if you've got a question for one of our panelists please do use the Q&A and we will absolutely make sure we get your question answered and that way we'll also make sure that we see it with all the great information coming in the chat the other piece that I wanted to know which Gus referenced and I believe Kristi noted in the chat earlier but we have created a term I'm stealing from Kristi Moss which is our virtual swag bag because today one of the things that we wanted to do is pull together a lot of information for you and have it in a handy place and so that is actually available as part of the resources with this webinar today so let me just pause for a moment and see do we have any questions that have come up or again please feel free to submit your questions or also Kristi, Gus, Kara anything that popped up in the chat that might be good to sort of just surface for the the group as well in terms of additional commentary. I think one of the things that I'm seeing in the chat are a lot of great uses of generative AI. Thank you all for sharing some that we we don't have on our list and might want to consider for future polls. One that I'm starting to see more and more often is generating code and there are a couple of different platforms that do this very well so that's becoming a more common way that folks are using it as well. Thanks to those in the chat who added those things yeah and I just wanted to highlight the text summary feature that people were highlighting that's definitely one that we've been using a lot and that is and another that I would highlight is kind of using generative AI as a starting point to brainstorm something to then build on. I know Kristi's going to have more examples of that later. And I do think it's interesting last well it feels like last year this year actually in support of CASE's 50th anniversary we've had a number of panel discussions that have happened at meetings and conferences and volunteer gatherings that asked the same question about what does the future of our profession look like in 50 years. We actually did some word clouds after doing some AI to analyze some of those pieces that came through and the idea that no matter what the sort of gathering was no matter who the professionals were the fact that AI is something that continues to come up in those conversations again I think is really critical as we're thinking about those next steps. Kristi I'm going to ask you to become Dr. Moss or Professor Moss for a few moments and have you just walk through a couple of demonstrations using chat GPT and as I mentioned at the beginning what we hope you all can do is follow along as Kristi's going through this. But before we move to demonstrations let's actually just shed some light on what we mean when we talk about some of the words and the platforms because I think you know Gus talked about cutting through the buzz and hype cycles I think we also have to cut through the buzz and all of these words we're hearing. Right right that sounds great thanks Jenny and as I'm talking I am going to get to some examples of how we can actually use generative AI together so if you are interested in doing that in a few minutes I would invite you to go ahead and log into chat GPT if you have that again no no pressure but we are going to try some things out today. Okay so I like to take a step back because you know kind of going back to that demystifying and getting rid of a little bit of the fear potentially around generative AI I like to start by talking about it as a tool and I feel like if we understand it as a tool we know when to use it how to use it and how not to use it. It's kind of one of those we would never use a hammer when a screwdriver is needed but often in this world of generative AI we kind of don't know the difference between some of these tools and we might use them interchangeably or for other things than they're meant for. So I like to start with the beginning of generative AI and I don't know how many of you have heard of the heard of the film The Imitation Game. It stars Benedict Cumberbatch and it's about an individual yes getting some thumbs up it's about an individual named Alan Turing and Alan Turing is often thought of as the father of AI and in the 1950s he was famously asked a question people asked him can computers think like people think and he was like wait I think you're asking the wrong question because at that time folks were saying listen we don't even know truly what it means to think as a human thinks so answering that is a little bit difficult. What he said is I actually think you should change the direction and you should say can we get computers to imitate hence the name The Imitation Game people in such a way that we can't tell the difference between a machine and a person and the Turing test was born which is something the Turing test and Turing awards are still given out to this day and the way the test goes is a series of questions are given to a machine a computer an AI program and also to an individual and then on the other end of that a person looks at the results of both and if they can't tell the difference between a human response and let's say a generative AI response then that particular generative AI has passed the Turing test and the types of questions that they ask you know some of them are punny and plays on words but some of them are questions like describe yourself as a color describe yourself as a shape things that have been traditionally thought as only something a human could do so if we move to the next slide this is where we talk about generative AI oh yep I got a little bit ahead of myself generative AI is really just machines or computers doing tasks that have historically been thought of as human only tasks and you were hearing an explosion of the word AI and products that you hadn't even been thinking about in that way now have that AI sticker slapped on them and you're like is anything different about this and in some cases it's not so we have things like like for example a chess game that's an early example of AI and it's a very small set of rules that then something is programmed on but what we're talking about today is generative AI and generative AI has many more layers stacked on top of it and isn't just one small set of rules for a computer to learn but it's many rules stacked on top of each other and it gets us these beautiful gen AI platforms and we're going to talk about four platforms today and how these four platforms can and should be used and how they should not be used so the first and most common that I think a lot of people are familiar with is chat gpt which is built by open ai it is very large it is a very flexible model it is broadly conversational it newly has a reasoner built into it if you're using a paid version and we'll talk about that a little bit later it's trained on a very large data set of text it was really kind of like a scraping of the open internet so everything from patents to reddit is in there and everything in between so what this model does is it looks for patterns in data um sorry patterns in words and then it predicts what should come next given the previous words in the pattern so this means it's taking all of those things from reddit and patterns of language in reddit but also free books that are online and other things like that nothing behind a paywall so not the new york times the new york times is not in chat gpt nor are any publications that are behind a paywall so it's kind of taking this language and summarizing it and figuring out based on those patterns what should go next so it's kind of like autocomplete on steroids what does this mean it is not in the traditional sense connected to the internet this means if you do a known search like you are searching for a fact that is out there it is going to hallucinate your answer and hallucination means it is going to confidently make something up that is absolutely not based in reality but just based in the pattern of language right that is what it is developed to do so when we put information into chat gpt like we feed it our marketing um or our brand statements or our language the way we like to talk then then we can ask questions based on that or ask for data or ask for paragraphs based on that and get something out that we can be confident about but if you go and say who was the you know you ask some question about a fictitious king or something like that it is going to give you um wildly crazy answers um and i'll get i'll give you another example of that in a minute claude so claude is really similar to chat gpt it's built by a company called anthropic and i have to tell you um yes the new version of web of chat gpt can search website and provide urls provided usually that you're using the paid version which we will get into that in a little bit as well so claude is usually it's made by anthropic the way that it was created is similar to chat gpt i will tell you that most academics prefer it because after it does the going through all of the search and looking at the pattern of data and coming to the response it's going to give you it goes through a second check and this second check prioritizes human safety prioritizes transparency prioritizes accuracy so it goes through kind of like a check of values and cloud put together anthropic put together a set of values and the responses are a little bit more safe and protect protected so this is an extra step that it goes through that chat gpt does not again there are paid and free versions of claude but to get the most out of these tools you certainly do have to pay for them going on to our next slide we're going to look at two more tools this is gemini and perplexity and i added these in because of the component of search so if claude and chat gpt are based on the idea of auto complete on steroids gemini and perplexity are like seo or search based optimization on steroids so gemini is actually searching the web when they're looking for this information and oftentimes now i will say all of these platforms they continue to evolve and they're making tweaks on the back end all the time and not always transparently and so you may have some slightly different results but the most for the most part gemini also in addition to giving you their response provides you with a list of links of where this information came from now the difference when you're talking about gemini and claude or chat gpt the quality of their written language is much better with those prior to so if you're just kind of like creating marketing copy for your own organization and you're not doing any type of search i would stay stick with the others but if you're doing some type of search you need to go with with gemini or perplexity now perplexity is not its own is not its own tool and we're going to start to see that more and more and more developing these generative ai models is incredibly expensive so most folks aren't they're just building tools on top of platforms that already exist and perplexity is actually built on top of chat gpt and claude and you can select which response you want it to use but it does then search the internet and everything every piece of information that it provides you it gives you citations and web sources so it says this is where it came from this is what we're citing this is kind of how we got the results that we got so again this has paid and free versions um yes chat gpt is linked to the internet but that does not keep it from making hallucinations because of the way that it will build that it is built so going on to this idea that tools are now being created on top of these programs you've probably heard of in some organizations use copilot copilot is a tool that is built on top of chat gpt so microsoft has taken chat gpt and infused it into their office products and that's where we get copilot so oftentimes these you know certain pieces can be taken and used in other ways and we're going to see a proliferation of these and one of the things that we can talk about you know are the ways to assess some of these tools but moving on i think we um we're going to get into now some of the practical uses for generative ai and this is where if you have the opportunity to go on chat gpt that would be great we're going to go through um one more slide and it's about setting your custom instructions and then i'm going to do a brief demonstration of that so if you can get into chat gpt this would be a great time to do it so when i talk to even rooms of users who have used chat gpt before most of them haven't gone about setting their custom instructions and this is a great opportunity actually to get even more tailored results than you might already be getting so i'm going to go ahead and share my screen now and we're going to walk through this process um jenny you might actually have to stop sharing in order for me to start sharing sorry about that thanks friends okay so i am going to well when i practiced this a little bit earlier it was slightly more elegant i apologize Okay, so this is my, this is what I have now, and I'm actually showing you my custom instructions, but I want to actually show you how I got to my custom instructions. So if you go here in the top right corner, you will see a photo of yourself or some type of icon over here that represents you. If you click on this, you will actually see this right here where it says Customize Chat GPT. It opens two windows here. What would you like Chat GPT to know about you to provide better responses? So I start by talking about where I work and the type of university I work at. I also say that I teach data science and marketing analytics, and that I consider myself an AI evangelist, and most of my interactions will be based on these two roles. So you have 1,500 characters here to describe the work that you do. Once you put this in, Chat GPT will always take this into account when it's providing responses to you. Then they also have another text box here to say, how would you like Chat GPT to respond? And this kind of gives you an idea of tone, but also, and this is where I like to get into this, I like Chat GPT to be questioning and to ask me questions when more clarity is needed. I value curiosity. I've also put in names here, but I prefer response with markdown format documents. So this is like, I like headings, and bold text, and italics, and I also like tables when appropriate. And here's the most key thing that I added in about how to respond. I say, let me know when your response is based on an opinion. And this is largely to keep Chat GPT from hallucinating. Okay. So I'm going to stop sharing my screen so that Jenny, you can go back to our slide deck. And this is, we're going to hear, get into a little bit more of our recommendations about how you use Chat GPT, but often it's to act as if. So when you start a prompt, do a little bit of an improv here. And you want to say, since this is a large language model, and you're trying to direct which type of language you want something to cue into, you want to start with saying, act as if you are a social media expert. Act as if you are an annual giving expert. Act as if you are a marketing expert. And you kind of act as if to provide this particular area of expertise. And you're going to see now, well, actually, we're going to go to our top do's and don'ts now for using generative AI. There are more of these in your swag bag, but our top three do's and don'ts. When you are having a conversation with Chat GPT, expect to have about eight back and forths. So you have about four turns, and Chat GPT has about four turns, and this will give you the best results. So part of this, when you're going in and you're saying, act as if, you are setting the stage, you are talking about the context in which you're asking these questions. And you should go back and forth to clarify and get better responses. The other do is absolutely copy and paste text from other publicly available web sources. So sometimes that's our own website. Sometimes that is not just our own website, but maybe you've got marketing language or you've got personas and you want to copy those in. Perhaps you say, I want you to be an expert in annual giving writing, and I'm going to provide you with our top eight segments and the personas that go with them. The next do is please revise your prompts. If you're getting bad results, don't keep going down and having a conversation. You can actually go back up to the very first prompt. There is a little pencil. You can click on the pencil and revise that prompt, and you will get better results. Our don'ts, never put in confidential information, which mostly, folks, we're thinking donor information, student information. Don't ever remove the human element. I like to say that ChatGPT is really good at being like a student worker, but you would still check their work before you started placing it online, at least at first. And finally, this gets into the tool situation, don't use ChatGPT as a web search tool only. And I know that right now it is starting to have web search tool capability. But what I mean by that is don't ask it a question that you can't verify with another source of information. So moving on now to our next set of slides, we're going to do a little bit of a demonstration again. And this is a brainstorming demonstration, and we're going to walk together through this to create a LinkedIn post. So we're going to go through a couple of slides, and then I'm going to share my screen again. So on our next slide, we see what we're going to go through to do this. Hopefully, some of you have already logged into ChatGPT and you want to do this. We're going to use our prompts we got on the next slide, and I'll also be putting some of this in the chat. And then we're going to find a story or a source, and I will let you know I'm going to be using a story that was put on LinkedIn by a case talking about some of their alumni relations work. And then we're going to think about how we might use this type of brainstorming for other tasks. So first, what we're going to do is put the prompt on the screen. And as we put the prompt on the screen, please note that it is in two parts, right? So we're going to do a little bit of this back and forth. And so the first thing that I'm going to do is put in, we're going to, let's see, go into our screen now. Jenny, do you mind if I start sharing again the demonstration screen? OK, so we're going to go back to our ChatGPT, and I'm going to share this with you, going to get out of my custom instructions here and go back to our chat. So what we want to do, I'm saying I would like you to serve as a social media expert. I'm going to provide you with text from an article, and I would like you to first provide me the summary of this text. And what you're going to need to do, I know that you, that ChatGPT says that you can just add a web link here. I have found particularly with the free versions and even sometimes the paid version, it actually helps to go over to your web source. And again, this is where I'm using, I'm using a LinkedIn article from Case, and I'm just going to copy paste this, going to copy paste the whole thing. And then we're going to go back over to ChatGPT, and I'm going to put it into ChatGPT. And the first thing that we're doing is asking just for a summary. And so the reason that we ask for a summary first is a couple of reasons. We want to make sure that it gets the entire article, that things don't get left out, and that we are also, that it is summarizing it correctly. So this is one of those ways that we kind of create a help and we make sure that we are not, we're not hallucinating. OK, so we're looking at this. I'm seeing the examples that I expect to see. I've read this a few times, so I know that it's correct. OK. So what I want to do now is put in our second prompt, and this is. This is please provide me. With five potential. LinkedIn posts based on this article. Each post should include a call to action. Appropriate hashtags. And the best practice principle. Each post illustrates. These provide. The results. In a table format. So there's one thing I want to call to your attention here is we say the best practice principle, anytime you brainstorm, if you do not say something like please provide the best practice principle that this brainstorm illustrates, you are going to get some wild stuff and maybe not really connected to what you're hoping to do. OK, so when we put that in. We're going to pretty quickly get a table that illustrates those things, OK? And what I am really interested in here, too, are what what it considers these best practice principles to be again, this is going back to making sure that what we're getting makes sense in the context that we're looking for it in. OK. So from here, this is where I would select something to further tailor it. So here's the post content. Here's our call to action. And then here's the best practice principle. So you're seeing this encourages multifaceted alumni connections beyond donations. This one is highlighting storytelling and actionable insights. This one's demonstrating the importance of tracking comprehensive engagement metrics. This one. So you see here that we've got some really great different best practices that we are considering. And this is all this is all very concise. And this is something, again, that took very, very little time. And so, Jenny, I think we want to hand it back over to you now. And, you know, before I pause in a funny spot, but I'm just going to keep talking. There we go. Before we sort of transition to some some use cases, let's actually just look at some of the questions that have come in as well, because there have been a lot of really great questions. I'm actually going to start with a couple that I think are more explicitly related to what you just shared, Christy, and then we'll back up to some that came in earlier. So the first one I said we would answer live because I think it's really important for everybody to hear. Is this customization and in terms of those setting your custom instructions only available in the paid version? It is not. It is available in the free version. And not only that, but most places like Gemini and Claude and Perplexity that have free versions, they also somewhere within their settings have some type of custom instructions and the free versions always have them as well. Another sort of, I guess, just asking your opinion, what would you say? Would you want an account for home and one for work so that you could better customize the responses? I do that. My office, I advocated for us to get an enterprise license, which is the only way that you're not sharing your data back with the parent company. And so I use that for my work stuff and it is customized around that. And then I have a different one for kind of like my my personal or like, for example, things I do with case or things I do in my classroom. Gus, a question for you from the earlier slides for the hype cycle that you were sharing, was that where Gen I is in relation to our industry, i.e. advancement or more broadly? So what I share with you is more broadly kind of overall use of technology. But there is a report that doesn't address as a hype cycle. But I think it's interesting is from Giffen's Tuesday AI readiness survey. It surveyed about like nine hundred and thirty people around the globe. And I think like five hundred something was from US. And they find out that 68 percent of people are using AI at the work, but only 28 percent are using in more than three ways, kind of either to to translate as Jenny, I ask about or a virtual assistant or different kind of things. So that gives a little bit of a landscape, specifically in the nonprofit sector. This is where the survey is about. But there's a little bit of a idea from it. And also from the same survey, they identify that the use of when they do like the what the tool is developed is delivering and what people want the tool to do. Generative AI is where there is the the most market demand match, I would say, from organizing information, predicting, interpreting information or assisting. They identify that this is where it is that the hardest, like what the tool is actually delivering and what people want the tool to be doing. So Jenny, I is actually the one that has the biggest market fit or what we want to use and actually what the tool delivers. So this also gives a little bit of understanding of where we are with the tool in the sector. And I'm actually going to go, I saw a question in the chat, so I'll address that and just a reminder for all of you, because of this amazingly active chat, if you put something in the chat and I missed it and it's a question, please just do post that in the Q&A. But Teresa, I see your question in today's polarized political climate where both progressive and conservative perspectives can offer valuable insights. How do we ensure that our AI systems remain grounded and accurate, factual knowledge while avoiding the influence of false narratives, slippery slope logic and other distortion that could be generated by ideological groups? What a fantastic and super easy to answer question, right? Colleagues, your thoughts. That gets to a lot of the ethical questions that are happening right now around generative AI and the ability of generative AI to very rapidly produce credible disinformation. So the short answer is there's not a way to ensure that this does not happen. And there are patterns of that in the data. And even as places like ChatGPT and Cloud, and Cloud is really working the best that they can to reduce that as much as possible, there are still biases within the data. And this is another reason that even when you're putting in a summary, you need to check. So early on, for example, you could take from Wikipedia an article on, let's say, scientists, OK, and there are all types of scientists listed in that article. And if you were to ask ChatGPT to give you a summary of that article, oftentimes it would leave out the women scientists and the piece and the scientists of color in its summary because it has noticed patterns in the data where bias exists. And so when it's loaded with biased data, biased data continues. So this is something that why we tell folks always look at what comes out of it, because bias does exist in the parent data. And we know that. And so you need to look at what comes out. So it's this idea of never removing the human element. And I was just going to add, Jenny, that this is really where having those principles of practice, having standards, having ways that you're using and acknowledging when you're using AI and having those checks in place becomes really important. And that's why the overall strategy and and what's expected and how transparent you are really matters. And that's something we emphasize a lot at CASE is transparency in what you're doing and highlighting the best use cases and principles. And actually, I think that's a really good segue to our next question, and that is, are you aware of any institutions that have developed and implemented AI guidelines institution wide? And are there examples to look to? Well, CASE has for us and the work that we're doing. And there's definitely other institutions that we could reach out to and see if there are things that we could share. But looking at just and that's something that I know a lot of institutions are working on as well. So we will find some examples to share out. And I'll also add it. That's a desired outcome of this project. When we talk about the research that CASE is conducting as well is to understand what are some of the barriers to adoption and what are some of those needs, too. And then I would add, I don't know if we have anyone from University of Texas on today, but our friend John Goff, who is an amazing technologist there, has a really good example using the donor Bill of Rights with the idea of, hey, CASE standards actually have a lot of components that address this already. Let's just make sure that it embraces this technology, too. We have at ISE one policy and guidelines. I'm happy to share. I can talk with my IT director. I can talk with my IT director. We'll be happy to share as well. Fantastic. Thanks, Gus. Thanks, Kara. A couple of other just specific questions again. Thank you all. Do you ever include audience parameters in your prompts, Christy? Yes, absolutely. Always. So this is part of the idea when we're talking about setting that stage and getting them to act as if I would start with the act as if you are a social media expert. And I would have it respond to me. And then I would say, so this is my situation. This is my audience. Provide as much context as possible and then make sure it responds to me. And then from there, I would start asking about what I'm here to do today and provide that response. We kind of condense some of that when we're talking about this live demonstration that we did in part just to kind of speed things up. But those are certainly definitely a part of that. And then usually at the end, kind of one more fine tuning back and forth to say, OK, I like this style or I don't like this style. You can also use tone and say, hey, we talk like this, not like this. Those kind of things help to refine your results as well. And let's go with one more question for now. And this was just a bit of a question to the do's and the don'ts that we had posted earlier and the notation about don't use AI as a web search. Can you just expand a little bit more on that? As Patrice, who posted the question, was just saying that she actually found by saying things like provide me with five pages, provide me with a URL link, et cetera, is working quite well. Yeah. So this is some new technology that OpenAI has actually released in the past few weeks. And I haven't checked it as of today, but most of these things are actually only available to paid users. So Patrice or others, if you are on a free version and it is allowing you to do that, absolutely update that. So in the past, that hasn't been available where it's been connected in that way. So we'll start to see if this is going to work. The other thing I would say about that is exactly the way that Patrice recommended is the way that you should do it, which is to say, give me these sources. So instead of just asking a broad, open-ended question, ask it to provide sources from these particular areas. So here's something to note too about this. First of all, if you are looking for sources, Gemini is built that way. ChatGPT is not built that way. So if you're just looking for sources, you're probably going to get better out of Gemini. If you're just looking for writing, you're probably going to get better out of ChatGPT. It's great to know what both are best at, even though they can both do other things as well. The other thing I would say, this is a space that's rapidly evolving. And every time I do one of these presentations, it's literally necessary to update the slides because some new functionality has occurred, some new features. So thanks for bringing that out as well. All right. Again, thank you all for your questions. I think even a couple more came in. But I do want to make sure we have a chance for Gus and Christy to really share some use cases that they found in their own shop. I'm going to share my screen again. And Christy, why don't you talk to us about Jackie? So I have two use cases here that I'm going to share and I'm going to do it a little bit briefly because I have provided within the swag bag a detailed guide that walks step by step through the steps that you would need to create this for yourself. But our idea here is Jackie I would take her with me everywhere if I could to kind of let you get to know her she is a wonderful bubbly personality she does really great at customer service. Those grumpy alumni they talk to her they're happy afterwards we love, we love to keep Jackie happy as well she creates a lot of great partnerships across our campus. As her role continued to evolve in our membership department. She needed to start producing marketing copy for these programs which included a landing page and email text subject lines, AB tests, social media, you kind of get it, it's a lot of text to write. She doesn't have a writing background. So this was taking her usually about two days a month and one of these was due every month. So what I did was go on the back end and if you have the paid version, or the enterprise version of chat GPT, you can create a custom GPT so what I did was put in, you know, again kind of, I think we had some questions about audience I talked about our audience. I talked about the language that we use in marketing that our alumni respond best to nostalgia and gamification, I gave it a few of our best, our best packages, our best marketing that folks have responded to. And I said, Okay, I need you to create for me all of these different pieces. And so now Jackie can go in. She can type in very plain language, very simply what our membership offering or package is. And then she can get all of this text that she needs which doesn't mean that she doesn't have to go in and edit it and update things, but she gets something to start from. So this went from taking her two days a month, right, which is 48. Nope. 24 sorry 24 days a year, down to 30 minutes, which is six hours a year so we went from 24 days to six hours you can see what a time savings that is to have Jackie really spend more time on those things that she likes the most and is best at. So that brings us to another example and this is around travel, and this is where we use a couple of different models and kind of with the last example this is also something that right now as of today is only true, you can only do with a paid version of chat GPT. So you can start here by defining like I want to, I'm coming in at this hotel, or I'm coming in at this airport I'm staying at this hotel. I've got these folks to meet around the city of let's say Chicago. I've got coffee meetings and lunch meetings. And these are all the little suburbs I'm going to be in. And if you go into chat GPT is reasoner, it's like 01 at the top of it it's a different version you can choose. And it will actually optimize that for you, it will say okay, if this is where you come in at and this is your hotel. These are the people you should see it on day one, here's day two, here's day three, here's day four, here's day five when you fly out and it will give you those based on, you know, you using public transportation or your own car or whatever it optimizes that for you, then you can take that optimized schedule schedule and actually plug it into perplexity and ask it to search for example for coffee shops. That are listed as Yelp on Yelp as quiet, or four and five star restaurants if you're looking for dinner options. And so you can kind of go from this took a lot of time to very clearly providing a very quick way for you or for your admin to help plan those development trips to see folks and again, all of the steps to do this are very detailed and laid out with examples of prompts that you can use and the outputs that you will get in the digital swag bag. Thank you. Jenny you're muted. Apologies for that. Thank you, Christy. Gus, can you share your experience and particularly from from a small school, or a small shop, I should say, small shop, small school medium school small shop. What I would like to share with you today is a little bit like use of AI, we have many examples and there's definitely more use to that, but I'm going to address like some specific ones which I think are the most interesting, also to be mindful of the time that we have. But it's really like how AI can play a role in the whole kind of progress of a data gathering analysis strategic planning and inside a campaign development and a campaign rollout so really kind of how it helped us organize this with the work that we do. So, really want to at ISC to understand and collect data so we have done many surveys with our community with donors with parents with students about learning as well. I'm going to be talking about my area but we have done this on the learning side of the school as well and really use AI to bring some questions that can have identify level of satisfaction and agreement with our branding. What do we want to do, and the outcome of that is really kind of a refinement of questions that we could use in a survey that will help us identify the level of agreement with our brand statements for ISC, the level of recommendation that parents have, and also the NPS kind of tool to measure that for each division that we have, and also kind of the level of satisfaction with multiple strategic areas from a school on a competitive landscape. We have done a lot of advancement as well from a interview with donors and non donors that we did and we kind of collect that in a different project but kind of with the same concept that we have. So this is how we can help collect data and really bring good information, the best information you put in the better outcomes you get out of it as well. So, in this analysis. What we looked at is, again, we have a leadership retreat, and imagine doing like a executive summary of your answers from the survey that like has 50 pages of open ended answers and comments and all this kind of things, how long would that take with AI and then you can have a two page summary of the key points of your survey which you can just like bring to the leadership team, or with your own team and have a very good conversation, but really kind of using the in this case we use chat GPT to do a sentiment analysis from the key takeaways you got from the survey like from even like from positive, but also like from the negative part of it, and then use that for strategic discussions, so where instead of wasting a lot of time to gather and organizing this information, you can just like jumping to have more significant and strategic conversations, and then this is just data that you can use later on to build your strategic plans for any area that you're working with. And then after you do this you want like some strategic insight and use AI for that right so from all this comments, we can create personas we can do like also as someone mentioned you can put links on chat GPT and it can do a little bit of a competitor analysis, you can get put your plan or put your strategy on and ask chat to check with what are your strategy addressing your goals and challenges, what are the gaps, and really what I like the most to do is to get AI to look at the strategy plan and ask me strategic questions about it. So it really helps you kind of find some blind spots that you plan might have, and give a little bit of different lenses where you can look at the work that you have, and then do some train analysis or identify areas of priority based on the comments and the data that you collected. So this really provides a comprehensive marketing plan and really addresses the data gather on the survey because what you want is really your strategy to address the information you gather from your community. So with all of that and with a plan in place, you can move on to the campaign development, and really from all the information that you gather you can define what is your value proposition and use AI to identify that, but connect that with a, what kind of communication the personas and the plan and kind of the areas that you identify in your data analysis and strategic insight phase. So, with this we even did like a photography style, what would be the best types of photography style that would match our audience and the people that we want to address within our strategic plan. So you can come up with editorial lines for it for content, you can, we created a communication installment style from that and also kind of a branding book that was built based on information we collected using AI. And then finally you get kind of the campaign rollout, where you have all of this amazing framework, done with AI, that you can do like Photoshop for a photo editing writing emails translation but really like we use social media, you can do a content schedule for it, which kind of provides a great and really quick idea brainstorming point for that. What it does and what we find out is that it helps create a balanced content distribution, because there are so many things we want to address and talked about, and we want to make sure that we are not just focusing in one topic and one specific aspect of our plan, but also to have like a coherent campaign rollout for that. It does increase a lot of time savings as well and you can focus on more relevant and I talked earlier with productivity and effectiveness of our work so that's definitely that. And I think personally thing is the most important part is really alignment with strategy and execution, because in between you can get a little bit lost into the things, but really kind of what AI will create on that final part of this whole process is also using what you provided first on that so when you doing your prompts or when you have your conversation with it, make sure that we also kind of do some check ins and ask like hey, based on the strategy that we develop what kind of content we can have. And I think just a tip for people share your concerns right what your concerns are from from what the prompt that AI is providing you and used to have a conversation that really kind of helps you bring clarity on that. I know we did really light speeding conversation, I just want to share a little bit of the scope and many different uses. And when you access the presentation, you can look at each point to that, but you know, this is a conversation and if you have any questions, feel free to reach out after the webinar on LinkedIn or anywhere else. Okay, so good luck using AI with your strategy and the work that you're doing. And now I think I'm passing on to Cara to talk about what is in the horizon. All right. We can go back and put that question up here. Go to you first. All right, well we would really want to. This the first part of a project we're doing funded by case 50 to really understand the how you're using AI and what the opportunities are in advancement. So this has been phase one education, and we're looking to move into a research study. So we're asking participants to join us for an asynchronous study over three days in the week of January 27, and it will be responding to a variety of prompts about your use of AI. So if you have an interesting use case we're going to be asking you to contribute to this study to be put your name into potentially be selected to be a part of this so we'll be sending out some details after the webinar and then we'll be sharing our findings from this in July and August of 2025. Great, and we will give you a prompt in a moment where you can tell us this sounds great. I'm really interested in filling out some information and potentially taking part. But before we do that, I, I know that you all have many great use cases. I feel like you've been very generous throughout with sharing them. We'd love just for you to take a moment. If you could scan the code that you see here, and you should get a prompt where you can just tell us briefly in a few sentences or a sentence or two. What is one of the most exciting or interesting things you've either seen or done yourself using generative AI? We heard a lot of great examples today. And so, as those are coming in, I will share my screen and a bit. I also will download the PDF of these responses and include it in our virtual swag bag as well. And I do want to so hopefully you all are seeing that you've been able to scan you're getting your answers and I'm going to stop sharing for a moment because I wanted us to just briefly address a couple of additional questions that have come in. I am so appreciative for all of the great questions you all have submitted. So, one question that came in was asking a little bit about some of the environmental implications to using AI. And the question is specifically sort of how is CASE thinking about this as we're having more information come out? And is this also something that potentially the research project could address? So, Kara, I'm going to pass it to you, but Gus or Christy, if you have anything to add on this topic, certainly feel free. I would say that right now we're continuing to monitor information, take in more information to figure out what guidance we may provide. A lot of institutions already have sustainability commitments as a part of the policies and practices that they're using. And I think that this starts to fit into that space as institutions are crafting their guidance. And so it's something we may be asking more people about in our research as well as we're doing some focus groups in addition to that asynchronous study of use cases. So, we are really looking at what are the best practices we're seeing emerge here and knowing that there's some frameworks that people already have in place. And this is something that's not unique to AI. We're seeing this with a lot of advanced computing use that impacts institutions that are members, whether that's in the work that advancement is doing or the work that researchers on our campuses are doing as they're innovating in various spaces. And so I think it's more surfacing that information already. We haven't assembled yet, but we've been definitely monitoring. Gus, a couple of questions as a response to some of what you shared. One, just a tactical question, do you do all of this through one tool such as ChatGPT or are you using various platforms? So, for all of that we use, we went from like ChatGPT and Gemini, depending also to test like what one would bring to us and what the other two would do it. But essentially we use the two of one, finding out, you know, you get some results and then you just went to try another AI to see what they bring up and just kind of being able to curate what these tools bring to us. So, these were the two that we use throughout the way. And I think one of the things I've loved in conversations with you is how transparent you've been with your audiences on that too. So, it's also a great learning opportunity. Absolutely. Another question. Oh, sorry. No, sorry. Yeah, we just have this as an institution that we will be fully transparent about it. You can see in some of our Instagram posts that there is a Gen AI kind of icon, our emails, our website and everything that we do here. So, yeah. And also just an interest in using AI for image generation specifically towards sort of the examples you were sharing. Right. So, we haven't like, we have used more AI for Photoshop for photo editing, rather than generating new images, especially like from human figures and all this. We haven't done that. I don't think we are approaching use of photographies like this. But there is like some Photoshop specifically has some great things like if I want to remove the background and just take one second where otherwise you have to go with the tool and cut people out. So, this has been like really, I think, been more effective in the way of using the tool and how we have been working with images and AI. And speaking of that, I'm going to go ahead and share my screen. Thank you all. I'm seeing some really fantastic responses coming in. And again, continue to feel free to submit those. But I know we are also 90 minutes flu. But you can see really nice examples here from developing a chatbot for Copilot. We're also actually doing that at CASE. Adding number of years. I am going to try to scroll down here. Sorry, Minty is not being my friend today. There we go. Adding number of years given to a list of records because the system only shows number of consecutive years. See endowment reports. Creating a 90 second commercial. So, a lot of these actually aligning with some of the things that we discussed today as well. I love this. Create a presentation for non-plan giving fundraisers that is clear and non-threatening. Beautiful. Well, I am going to just stop sharing one more time. I know we are right at time. And actually, maybe I was not even sharing there. Apologies for that. And I think we had one more question coming in. Though I am actually not able to see it. I don't know if my colleagues can pull that up. I can see Jenny just getting to it here. To edit audio video recordings. Has anyone used AI for that purpose? I am looking at Christy because I feel like you are our most likely candidate on this screen. It certainly can. It is not something that I have regularly done. I have kind of made some of these AI videos before and where it kind of pulls in different things like that. So, the technology is certainly available and out there. I have one example that we have tried now. We have recorded a sentence from a person here at school that has a very good voice. And we are using AI to kind of be the voiceover in our videos. So, then you just put the voice in there. The text, what do you want the person and then kind of uses this voice to do the voiceovers. So, it has been very interesting. Not always perfect. Sometimes it looks very robotic. But we have been trying to see how does the technology work for us in that sense. Great. Well, we are at time. Thank you all so much. Again, really appreciative of how interactive you kept this today. And as you can see, we have got our prompt on the screen. So, if you are interested in helping us further this research, now is an opportunity for you to let us know. And then we will follow up with you with next steps. And particularly, we are incredibly thankful for Christy and Gus for sharing their expertise with us today. Thank you all. Thank you everyone for being here. There is one question of recorded. I think it is important to share. There is a recording. And also, there is another opportunity to catch this session later today for me. But tomorrow for some people, which we have two different time periods we offer things in so that we can catch all the regions where a case serves. All right. Well, thank you everybody. Glad to hear that this has been a good use of your time today. That was exactly what we were hoping for. Thank you so much. Have a great day. Bye-bye. Thank you.

generative AI

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Gustavo Seguin

CASE

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